Cell cycle status and apoptosis of hematopoietic progenitor cells released into the peripheral blood after taxanes and granulocyte colony-stimulating factor in breast cancer patients.
Bibliographic record
Abstract
Paclitaxel and its analogue docetaxel show a significant antitumor activity, particularly evident in breast cancer. Paclitaxel has also been proved to be effective as a peripheral blood progenitor cell (CPC) mobilizing agent. To optimize the use of active, disease-specific drugs as CPC priming, we have evaluated the effects of either paclitaxel or docetaxel both at standard dosages and followed by granulocyte colony-stimulating factor (G-CSF), on circulating CPC release and function in 18 patients with advanced breast cancer who had failed previous anthracycline-based regimens. The reported differences in biological behaviour between bone marrow and blood-derived hematopoietic progenitor cells and the ability of both paclitaxel and docetaxel to induce apoptosis, prompted us to simultaneously evaluate the cell cycle perturbations induced on CD34+ cells. Median CD34+ peaks were 24 microl (range: 10-58) in the paclitaxel-treated patients and 39 microl (range: 17-91), respectively, in the patients who received docetaxel. After paclitaxel, the percentage of CD34+ cells in S-phase was low (bromodeoxyuridine, BrdU, labelling index = 3.4+/-2%) with a concomitant presence of early apoptotic cells (8.1+/-3%). On the contrary, after docetaxel, the percentage of CD34+ cells in S-phase was higher (BrdU labelling index = 14.5+/-4%, p<0.05 vs. paclitaxel), while early apoptotic cells were detected at a similar rate (8. 6+/-3%, p = n.s. vs. paclitaxel). In conclusion, when used at standard dosages, with respect to paclitaxel + G-CSF, docetaxel + G-CSF is a more satisfactory tool to mobilize CPC and to induce them into the cell cycle. These data should be taken into account when combinations of docetaxel with other agents are explored as CPC mobilizing regimens for autografting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".